Authors: Jennifer Roberts, Rebecca Turner, Victoria Hughes, Richard Morgan, Chaitanya Srinivas, Akhilesh Achari
Abstract: Cloud-native architectures have become the foundation of modern digital applications due to their scalability, flexibility, resilience, and ability to support continuous deployment across distributed computing environments. However, the increasing complexity of microservices, containers, orchestration platforms, and dynamic workloads introduces significant challenges in maintaining system reliability and preventing service disruptions. Traditional reactive maintenance approaches often fail to identify potential failures before they impact application performance and user experience. This research presents a predictive failure analysis framework for reliability engineering in cloud-native architectures that leverages predictive analytics, machine learning algorithms, real-time monitoring, and intelligent fault detection mechanisms to proactively identify and mitigate system failures. The proposed approach continuously analyzes operational metrics, infrastructure logs, service dependencies, and workload patterns to detect anomalies, forecast potential failures, and recommend corrective actions before critical incidents occur. By integrating predictive models with cloud-native reliability engineering practices, the framework supports automated fault diagnosis, resource optimization, resilience enhancement, and service continuity. The study explores key architectural components, predictive techniques, reliability metrics, and implementation strategies for building highly available and fault-tolerant cloud-native systems. Experimental evaluation demonstrates improvements in failure prediction accuracy, system uptime, response performance, and operational efficiency compared to conventional monitoring methods. The findings indicate that predictive failure analysis provides a robust foundation for developing intelligent, adaptive, and resilient cloud-native infrastructures capable of supporting the growing demands of modern enterprise applications and distributed computing ecosystems.
DOI: http://doi.org/10.5281/zenodo.20797204
